• New & Renewable Energy
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• v.2 no.2 s.6
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• pp.81-85
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• 2006

The output performance characteristics for surveyed sites were analyzed, using developed model. It consists of two main parts, the deciding flow duration characteristic of river and performance prediction model to estimate the output characteristics of small hydropower plants. As a result, It was found that the flowrate concerning with 25% of time ratio on flow duration curve can be selected to design flowrate of small hydropower plants, and the output characteristics of small hydropower plants having overflow dam are different from large scale hydropower plants.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.8
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• pp.68-74
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• 1998

The neural network predictiv econtroller (NNPC) is proposed for the attempt to mimic the function of brain that forecasts the future. It consists of two loops, one is for the prediction of output (NNP:neural network predictor) and the other one is for control the plant(NNC: neural network controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and predictin error. The NNP forecasts the future output based upon the current control input and the estimated control output. The input and the output data of a system and a new method using evolution strategy are used to train the NNP. A two-step NNPC is applied to control the temeprature in boiler systems. It was compared with PI controller and auto-tuning PID controller. The computer simulaton and experimental results show that the proposed method has better performances than the other method.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.491-494
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• 2006

The output performance characteristics for surveyed sites were analyzed, using developed model. It consists of two main parts, the deciding flow duration characteristic of river and performance prediction model to estimate the output characteristics of small hydropower plants. As a result, it was found that the flowrate concerning with 25% of time ratio on flow duration curve can be selected to design flowrate of small hydropower plants, and the output characteristics of small hydropower plants having overflow dam are different from large scale hydropower plants.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1222-1225
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• 1993

The purpose of this paper is to construct a prediction system on the chance of rain in a local region using a fuzzy relational model. The prediction system consists of two parts. One is a prediction part on the chance of rain. The compositional law of fuzzy inference, proposed by Zadeh, is applied to predict the chance of rain. The other is a learning part of a fuzzy relational model using input-output data. A simple and fast learning algorithm is used in this part. Simulations are carried out by the actual weather data in our city and their results show the validity of prediction by the fuzzy relational approach.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• New & Renewable Energy
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• v.7 no.3
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• pp.36-45
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• 2011

Prediction of demand for workforce in new and renewable energy is precondition for sustainable growth of an industry. The purpose of this research is to review prediction methods and case studies of workforce in new and renewable energy industry. This research compares the three methods in the focused on possibility of applying in renewable energy industry; survey, input-output and labor function estimation methods. Also, three cases are reviewed in the focused on applied method; Korea, America and Australia. As a result, the survey method was wildly used in the new and renewable industry. Also the improvement rates of work force are difference depending on the methodology. This result can be applied to set up the policy of human resource development of renewable energy.

• East Asian Economic Review
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• v.16 no.3
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• pp.227-247
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• 2012

This paper investigates the source of bilateral trade imbalance at industry level. We build a simple model based on gravity theory and derive the prediction that the bilateral trade balance in an industry is increasing in the difference between trading partners in the output share of the industry. We test this prediction and find that the difference in industry share is highly significant in predicting both the sign and the magnitude of trade balance at industry level. We also find that FTAs tend to enlarge trade imbalance at industry level. However, the overall predictive power of the model is rather limited, suggesting that factors other than production specialization are important in determining trade balance at industry level. Another finding of the paper is that the influence of the difference in industry share on trade balance increases as we move to industries that produce more homogeneous products. This finding calls into question monopolistic competition as the main driver of gravity in international trade.

• Journal of Power Electronics
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• v.17 no.1
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• pp.1-10
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• 2017

This paper presents a novel predictive digital control method for boundary conduction mode PFC converters without the need for detecting the inductor current. In the proposed method, the inductor current is predicted by analytical equations instead of being detected by a sensing-resistor. The predicted zero-crossing point of the inductor current is determined by the values of the input voltage, output voltage and predicted inductor current. Importantly, the prediction of zero-crossing point is achieved in just a single switching cycle. Therefore, the errors in predictive calculation caused by parameter variations can be compensated. The prediction of the zero-crossing point with the proposed method has been shown to have good accuracy. The proposed method also shows high stability towards variations in both the inductance and output power. Experimental results demonstrate the effectiveness of the proposed predictive digital control method for PFC converters.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.2198-2204
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• 2003

In order to make a match of the insufficient direct solar radiation, in this study, the target output is lowered to 9 kW smaller than 25 kW in former studies. It is also necessary to match the collector/receiver with engine/generator systems to accomplish the power level of a system. The simulation analyses of a dish solar power system with stirling engine are totally carried out to predict the system performance with the designed values. In addition, an influence of direct solar radiation on system performance and operation control is discussed in simulation. It is found that the diameter of concentrator could be made small to 8 m regardless of slope errors with 2.5 and 5.0 mrad radiation, and the operation range of mean pressure control. is wide even if the direct solar radiation is a quit low.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2056-2059
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• 2003

The rate control is very important to solve the difficulties arising from bit-rate on transmission through channel and to improve video quality. It is very important to point out that the amount of output bit obtained the encoding process using rate controller brings many problems on the transmission of channels and furthermore output bitstream decoded affects directly on the visual quality of displayed subject. In this paper, the effective rate control algorithm by rate-distortion modeling using MPEG-4 encoder is proposed. The proposed rate control has applied different weighting by VOP prediction type and even in the same VOP prediction type, the predicted reference allocates more bit. Through these bit allocation the minimization of distortion can be achieved preventing propagation of quantization error The amount of saved bitstream obtained by the proposed algorithm in this thesis is allocated to I-VOP using region of interest(ROI) selective enhancement on the next GOV encoding process and this process brought the improvement of visual quality.